We spent too much time asking how sophisticated the AI architecture could become.
A custom model seemed like the stronger long-term answer. The problem genuinely involved classification, so the decision wasn’t irrational.
But we were solving the technical problem more completely than the initial product required.
The customer didn’t care whether the result came from a custom model, retrieval, graph relationships, matching or deterministic rules. They needed the product to recognize enough risk and intervene reliably.
That changed how I think about AI product architecture.
Before adding more intelligence, I now ask:
What level of reliability does the customer actually need?
Can retrieval, matching and rules reach that threshold?
What new operational burden does the more advanced model create?
Will the added complexity improve the customer outcome—or mainly make the architecture more impressive?
Sometimes the right AI product needs less AI.
The goal is not to use the most sophisticated architecture available. It is to use the simplest architecture that can reliably produce the outcome.
Good teams don’t always get stuck because of bad decisions. Sometimes they get stuck because too many reasonable decisions happen before the market gets a vote. This is the MVP trap I’ve fallen into myself. https://t.co/GkI5leWVuE
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